Learning optimal Kernel from distance metric in Twin Kernel Embedding for dimensionality reduction and visualization of fingerprints

Biometric data like fingerprints are often highly structured and of high dimension. The "curse of dimensionality" poses great challenge to subsequent pattern recognition algorithms including neural networks due to high computational complexity. A common approach is to apply dimensionality reduction (DR) to project the original data onto a lower dimensional space that preserves most of the useful information. Recently, we proposed Twin Kernel Embedding (TKE) that processes structured or non-vectorial data directly without vectorization. Here, we apply this method to clustering and visualizing fingerprints in a 2-dimensional space. It works by learning an optimal kernel in the latent space from a distance metric defined on the input fingerprints instead of a kernel. The outputs are the embeddings of the fingerprints and a kernel Gram matrix in the latent space that can be used in subsequent learning procedures like Support Vector Machine (SVM) for classification or recognition. Experimental results confirmed the usefulness of the proposed method.